1. Introduction: The wind is used for solar energy, and solar energy is used for wind energy. Without each other, electricity cannot be made. Then based on the components, the generator, and the inverter-related electricity can be saved later. Problem formulation: One of the main problems with solar and wind energy is that they make non-concentrated and dilute energy from vast lands. Also, generating the variability and the cost factors is the problem in wind and solar power. To solve this, the installation of solar panels has been enabled for the energy done. The production of the batteries in some of the solutions can be stimulated for the analysis is done. One technique used in this paper is the Artificial neural networks-based expert system and the crop production system. Techniques: Artificial Neural Network-Based Expert Systems are used to predict the plant response in the environment, which is the response to the humidity, light radiation, and temperature. The crop production system is used for performing the plant performance, and the fertilizer follows the resources of the plants and other arrangements related to the plants. Result: The results from the working of the wind and solar energy are equally proportional to the analysis’s 50% -the 50s. Then, the suggested model explains 96.9% of the variation in the dependent variable (plant growth and development) based on the input variables (temperature, CO2, humidity, and light radiation), according to the Coefficient of Determination of 0.969. Overall, the suggested ANN-ES model anticipates plant growth and development based on the input variables and is regarded as dependable for this purpose.

This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle’s low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.

The breast cancer (BC) prediction is improved through the machine learning (ML) techniques. In this study, we develop an innovative forecasting framework called the Dynamic Harris Hawks Optimized Gated Recurrent Unit (DHH-GRU) for the prediction of BC. It combines the Gated Recurrent Unit (GRU) and Harris Hawks Optimization (HHO) methods. We gathered data and a training set that included the Wisconsin diagnostic BC (WDBC) dataset, which contains 569 patients with malignant and beginning cases. The collected data were pre-processed using min–max normalization, and important features were extracted by Fast Fourier transform (FFT) and the process of reducing the dimensionality with principal component analysis (PCA). Decimal scaling is employed to equalize the various feature effects. The proposed DHH-GRU technique incorporated the GRU for capturing sequential connections on temporal medical information, and the optimization process, DHH optimization, is utilized. The proposed method's effectiveness is compared and estimated with various existing techniques in terms of log-loss (0.06%), accuracy (98.05%), precision (98.09%), F1-score (98.28%), and recall (98.15%). The proposed DHH-GRU method has a more predictive ability with the sequential dependency in capturing GRU and DHH optimization’s combined behaviour of hunting. This method significantly improved the accuracy of BC prediction.

The hazards associated with cyber security attacks have increased due to our increasing reliance on digital technologies. We suggest an ensemble strategy employing multiple models of machine learning to get over these restrictions. Our suggested approach detects an extra 141 dangerous URLs, outperforming the single model that is currently in use by 6%. We gathered a collection of malicious and benign URLs for this investigation. Applying Principal Component Analysis (PCA) to feature extraction. Subsequently advantageous features are selected and categorised using the Social Spider optimised Densely-connected Convolutional Caps net (SSO-DCN). The most effective detection frameworks and automated methods for identifying rogue web pages, including those distribute malicious malware, are included in the proposed technology. When comparing the proposed method to the existing approaches, the improvements are in mean accuracy (96.30%), precision (96.90%), F1-Measure (97.20%), recall (96.90%), FPR (3.50%) and FNR (3.90%).